Today, speech-to-text systems are commonly used for speech-mining in call center applications as they can give rich speech to text outputs that can be used for many different information retrieval purposes.
However, speech recognition performance of such systems degrade due to some harsh conditions such as background speech and noises, huge speaking style variability of the speakers and high perplexity of largely varying request content. This degradation in speech-to-text performance may affect the following analysis, and decrease the reliability of the statistics inferred from the speech-analytics system:                Agent voice's monotonicity        Agent speaking rate        Agent/customer interrupt, block speaking counts        Dialog-based analysis        
Speech-to-text systems also demand powerful analysis servers as the speech recognition module is also highly CPU-intensive. This situation creates extra need for hardware and increases the overall costs excessively.
In the prior art, speech analytics systems are gradually being improved and alternatives are being created for making accurate statistics into such kind of systems. However, accuracy rates of these input alternatives are not much higher.
In conclusion; improvements are being made in the methods providing speech analytics and transmission of a speech as a text into hardware with minimum error, therefore new embodiments eliminating the disadvantages touched above and bringing solutions to existing systems are needed.